Day’s objectives
- Understand workflow, why it is important, and how to do it using Rstudio/R
- Have an understanding of the tidyverse framework and its key packages
- Work through an example
Key packages

Example of some workflows
mine
Paul’s
Commonalities
Advantages of workflow
asfd
adsf
Dataset for workshop
link to .
Rstudio
You can find a variety of cheatsheets at https://posit.co/resources/cheatsheets/
Basics
- assume familiar with
- might want to turn off .Rdata so everything is fresh on startup (no issues with previous objects so completely reproducible)
Tips and tricks
- tab
- fills in paths
- hexcode color coating
- code folding (#)
- insert pipe: ctrl+shift+M %>%
- comment: ctrl + shift + C
- find in all files
Programming good technique
- consistent style
- modularize your code (functions - slightly more advanced)
Tidyverse framework

Historical context
quirky things about R: factors vs character, NAs,
tibble is data.frame v2.0
- better printing of the data
- handles some of the quirky things that caught up people
- column names
lubridate fixed date quirks
Importing
Excel files

CSV/Table

Joins

knitr::include_graphics(“dplyr.pdf”)
Note: joins = merges (synonyms)
Tips and tricks
- Essential to make sure that the number of rows out matches your expectation
- Almost all my joins are
Restructuring

knitr::include_graphics(“tidyr.pdf”)
String manipulation

knitr::include_graphics(“stringr.pdf”)
Date/time

knitr::include_graphics(“lubridate.pdf”)